Centre de documentation
scientifique

Comparison of multi-seasonal Landsat 8, Sentinel-2 and hyperspectral images for mapping forest alliances in Northern California / Matthew L. Clark in ISPRS Journal of photogrammetry and remote sensing, vol 159 (January 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 159 (January 2020) . - pp 26 - 40 Titre : Comparison of multi-seasonal Landsat 8, Sentinel-2 and hyperspectral images for mapping forest alliances in Northern California Type de document : Article/Communication Auteurs : Matthew L. Clark, Auteur Année de publication : 2020 Article en page(s) : pp 26 - 40 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] analyse comparative [Termes IGN] apprentissage automatique [Termes IGN] Californie (Etats-Unis) [Termes IGN] carte forestière [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] couvert végétal [Termes IGN] image AVIRIS [Termes IGN] image hyperspectrale [Termes IGN] image Landsat-8 [Termes IGN] image Sentinel-MSI [Termes IGN] occupation du sol [Termes IGN] Short Waves InfraRed Résumé : (Auteur) The current era of earth observation now provides constellations of open-access, multispectral satellite imagery with medium spatial resolution, greatly increasing the frequency of cloud-free data for analysis. The Landsat satellites have a long historical record, while the newer Sentinel-2 (S2) satellites offer higher temporal, spatial and spectral resolution. The goal of this study was to evaluate the relative benefits of single- and multi-seasonal multispectral satellite data for discriminating detailed forest alliances, as defined by the U.S. National Vegetation Classification system, in a Mediterranean-climate landscape (Sonoma County, California). Results were compared to a companion analysis of simulated hyperspectral satellite data (HyspIRI) for the same study site and reference data (Clark et al., 2018). Experiments used real and simulated S2 and Landsat 8 (L8) data. Simulated S2 and L8 were from HyspIRI images, thereby focusing results on differences in spectral resolution rather than other confounding factors. The Support Vector Machine (SVM) classifier was used in a hierarchical classification of land-cover (Level 1), followed by alliances (Level 2) in forest pixels, and included summer-only and multi-seasonal sets of predictor variables (bands, indices and bands plus indices). Both real and simulated multi-seasonal multispectral variables significantly improved overall accuracy (OA) by 0.2–1.6% for Level 1 tree/no tree classifications and 3.6–25.8% for Level 2 forest alliances. Classifiers with S2 variables tended to be more accurate than L8 variables, particularly for S2, which had 0.4–2.1% and 5.1–11.8% significantly higher OA than L8 for Level 1 tree/no tree and Level 2 forest alliances, respectively. Combining multispectral bands and indices or using just bands was generally more accurate than relying on just indices for classification. Simulated HyspIRI variables from past research had significantly greater accuracy than real L8 and S2 variables, with an average OA increase of 8.2–12.6%. A final alliance-level map used for a deeper analysis used simulated multi-seasonal S2 bands and indices, which had an overall accuracy of 74.3% (Kappa = 0.70). The accuracy of this classification was only 1.6% significantly lower than the best HyspIRI-based classification, which used multi-seasonal metrics (Clark et al., 2018), and there were alliances where the S2-based classifier was more accurate. Within the context of these analyses and study area, S2 spectral-temporal data demonstrated a strong capability for mapping global forest alliances, or similar detailed floristic associations, at medium spatial resolutions (10–30 m). Numéro de notice : A2020-011 Affiliation des auteurs : non IGN Thématique : FORET/GEOMATIQUE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.11.007 Date de publication en ligne : 14/11/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.11.007 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94399 [article]

Exemplaires(3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2020011	RAB	Revue	Centre de documentation	En réserve L003	Disponible
081-2020013	DEP-RECP	Revue	LASTIG	Dépôt en unité	Exclu du prêt
081-2020012	DEP-RECF	Revue	Nancy	Dépôt en unité	Exclu du prêt

Mapping of land cover in northern California with simulated hyperspectral satellite imagery / Matthew L. Clark in ISPRS Journal of photogrammetry and remote sensing, vol 119 (September 2016)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 119 (September 2016) . - pp 228 - 245 Titre : Mapping of land cover in northern California with simulated hyperspectral satellite imagery Type de document : Article/Communication Auteurs : Matthew L. Clark, Auteur ; Nina E. Kilham, Auteur Année de publication : 2016 Article en page(s) : pp 228 - 245 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] base de données d'occupation du sol [Termes IGN] Californie (Etats-Unis) [Termes IGN] carte d'occupation du sol [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] image hyperspectrale [Termes IGN] interprétation automatique [Termes IGN] occupation du sol [Termes IGN] simulation d'image Résumé : (Auteur) Land-cover maps are important science products needed for natural resource and ecosystem service management, biodiversity conservation planning, and assessing human-induced and natural drivers of land change. Analysis of hyperspectral, or imaging spectrometer, imagery has shown an impressive capacity to map a wide range of natural and anthropogenic land cover. Applications have been mostly with single-date imagery from relatively small spatial extents. Future hyperspectral satellites will provide imagery at greater spatial and temporal scales, and there is a need to assess techniques for mapping land cover with these data. Here we used simulated multi-temporal HyspIRI satellite imagery over a 30,000 km2 area in the San Francisco Bay Area, California to assess its capabilities for mapping classes defined by the international Land Cover Classification System (LCCS). We employed a mapping methodology and analysis framework that is applicable to regional and global scales. We used the Random Forests classifier with three sets of predictor variables (reflectance, MNF, hyperspectral metrics), two temporal resolutions (summer, spring-summer-fall), two sample scales (pixel, polygon) and two levels of classification complexity (12, 20 classes). Hyperspectral metrics provided a 16.4–21.8% and 3.1–6.7% increase in overall accuracy relative to MNF and reflectance bands, respectively, depending on pixel or polygon scales of analysis. Multi-temporal metrics improved overall accuracy by 0.9–3.1% over summer metrics, yet increases were only significant at the pixel scale of analysis. Overall accuracy at pixel scales was 72.2% (Kappa 0.70) with three seasons of metrics. Anthropogenic and homogenous natural vegetation classes had relatively high confidence and producer and user accuracies were over 70%; in comparison, woodland and forest classes had considerable confusion. We next focused on plant functional types with relatively pure spectra by removing open-canopy shrublands, woodlands and mixed forests from the classification. This 12-class map had significantly improved accuracy of 85.1% (Kappa 0.83) and most classes had over 70% producer and user accuracies. Finally, we summarized important metrics from the multi-temporal Random Forests to infer the underlying chemical and structural properties that best discriminated our land-cover classes across seasons. Numéro de notice : A2016-783 Affiliation des auteurs : non IGN Autre URL associée : Thématique : GEOMATIQUE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2016.06.007 En ligne : https://doi.org/10.1016/j.isprsjprs.2016.06.007 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82480 [article]